ArticleSensors (Basel, Switzerland)2021
Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.European journal of orthodontics · 2024Pooled it
- Comparative analysis of generative pre-trained transformers for text- and image-based cephalometric prompts using a novel Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI).BMC medical informatics and decision making · 2026Article
- Artificial Intelligence Methods in Cephalometric Image Analysis-A Systematic Narrative Review.Journal of clinical medicine · 2026Review
- Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.BMC oral health · 2025Article
- Chronology of orthognathic surgery.Journal of the Korean Association of Oral and Maxillofacial Surgeons · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Accuracy and reliability of 3D cephalometric landmark detection with deep learning.European journal of medical research · 2025Article
- Automated Measurements of Tooth Size and Arch Widths on Cone-Beam Computerized Tomography and Scan Images of Plaster Dental Models.Bioengineering (Basel, Switzerland) · 2024Article
- Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm.Scientific reports · 2024Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Sex estimation using skull silhouette images from postmortem computed tomography by deep learning.Scientific reports · 2024Article
- Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression.Scientific reports · 2023Article
- SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.Scientific reports · 2023Article
- Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review andThe Saudi dental journal · 2023Review
- Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.